AI Agentic Solutions Engineer

Title: AI Agentic Solutions Engineer

Duration: 3 Months

Location: Seattle, WA, 98101

Interview Process: Ask for Agency Screening to be uploaded with resume, 2 interview 1 with manager 1 with engineer Technical round.

As a Engineer 2, you are a lead individual contributor responsible for the quality of a team’s work and capable of tackling complex design and problem solving without supervision. You are a product-minded engineer — you design systems spanning multiple weeks or months of work, hold a strong point of view on what good agent user experience looks like, make technical decisions that balance short and long-term business objectives, and take ownership of team-level costs and metrics. You will champion new techniques, mentor junior engineers, and be a key technical voice in cross-functional discussions.

A Day in the Life

• Partner with business and technology stakeholders to define the “art of the possible” with agents — translating ambiguous problems into agentic solutions with clear success criteria and measurable outcomes.
• Design and build core agentic solutions end-to-end across orchestration, tool-use pipelines, and integration with enterprise systems.
• Own end-to-end solution design for agentic solutions spanning multiple engineers’ work, with full upstream/downstream integration consideration.
• Apply context engineering to determine what an agent sees, when, and why — balancing token economics, latency, and decision quality across RAG patterns, structured retrieval, and dynamic prompt assembly.
• Develop and own evaluations and guardrails that demonstrate solutions are safe, reliable, and accurate — offline benchmarks, online production telemetry, and failure-mode analysis.
• Architect memory and state management approaches that let agents reason across sessions, users, and workflows — short-term context, long-term memory, and durable conversation state.
• Apply AI fluency to integrate LLM APIs, embedding models, vector stores, and agentic frameworks into production services; evaluate and adopt emerging techniques as appropriate.
• Make and clearly articulate technical trade-offs between short-term delivery needs and long-term scalability, factoring in design, framework choice, model selection, and infrastructure costs.
• Design systems accounting for current and upcoming product cycles, team-level cost responsibility, and alignment with cross-functional roadmaps.
• Lead design and code reviews across the team; provide actionable feedback and maintain a high bar for quality, testability, and extensibility.
• Design key metrics, evaluations, and observability patterns for agentic solutions; drive accountability for performance, cost, accuracy, and security of feature work.
• Work with business, infrastructure, and security teams to deliver enhancements, reliability improvements, and bug fixes for production AI systems.
• Surface potential design or delivery conflicts in the current or upcoming product cycle and make clear recommendations on the best path forward.
• Mentor and support junior engineers across a wide spectrum of technical activities; participate in hiring interviews with clear, specific feedback.
• Ensure own work and team members’ work follows Client’s engineering and security standards; contribute to those standards

Skills:

• 6 years of professional software engineering experience, with a strong track record of designing and delivering complex, scalable distributed systems.
• AI Fluency — Required: Hands-on experience working with LLMs, foundation model APIs (OpenAI, Anthropic, Google, etc.), prompt engineering, retrieval-augmented generation (RAG) architectures, and embedding-based search in production environments.
• Experience designing, building, and operating AI agents or agentic workflows in production, including tool-use, orchestration, and integration with downstream systems.
• Strong understanding of how to assemble, prune, and structure context for agents to maximize decision quality within token, latency, and cost constraints.
• Experience designing evaluation frameworks and safety guardrails for LLM-based systems, including offline benchmarks, online telemetry, and responsible deployment practices.
• Familiarity with short-term and long-term memory patterns for agents, vector stores, conversation state, and durable workflow state.
• Hands-on experience with agentic frameworks such as Claude Agent SDK, LangGraph, AutoGen, CrewAI, Semantic Kernel, or OpenAI Assistants API.
• Familiarity with multi-agent orchestration patterns: task decomposition, tool-use pipelines, and human-in-the-loop workflows.
• A product-minded approach to engineering: strong instincts for user impact, comfortable pushing back on requirements when the right solution isn’t the one initially asked for, and able to translate business intent into agentic capabilities.
• Proficiency in Python; strong grasp of multiple tech stacks and cloud-native development on AWS and/or GCP.
• Experience working with cross-functional teams including product, business, infrastructure, and security stakeholders.
• Strong verbal and written communication skills; ability to articulate complex technical decisions to both technical and non-technical audiences.
• Agile development experience (Scrum, Kanban, Lean, or similar) with a continuous improvement and quality mindset.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent practical experience.

Nice to Have

• Experience with RESTful services, event-driven architectures, and backend databases (SQL, NoSQL, or cloud-native datastores).
• Familiarity with containerization technologies (Kubernetes, Docker) and modern CI/CD practices and tools (e.g., GitLab).
• Strong emphasis on building observability into systems — real-time alerting, dashboards, metrics, and performance accountability.
• Background in retail, e-commerce, or supply chain domains — understanding of how AI agents can drive value in inventory, fulfillment, personalization, or customer service.
• Experience with big data technologies (Spark, BigQuery, Redshift) and integrating ML models into production services.
• Contributions to open-source AI projects; curiosity and engagement with the broader AI/ML engineering community